Using a profiler such as gcov or gprof , you can find out some basic performance statistics, such as: Tag Description o how often each line of code executes o what lines of code are actually executed o how much computing time each section of code uses Once you know these things about how your code works when compiled, you can look at each module to see which modules should be optimized. Software developers also use coverage testing in concert with testsuites, to make sure software is actually good enough for a release. Testsuites can verify that a program works as expected; a coverage program tests to see how much of the program is exercised by the testsuite. Developers can then determine what kinds of test cases need to be added to the testsuites to create both better testing and a better final product. Likewise, because gcov accumulates statistics by line at the lowest resolution , it works best with a programming style that places only one statement on each line.
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Any large code base needs to be incrementally tested for each release for regressions and bugs. When the code base grows, manual testing fails to meet the requirements and we have to move into automated systems for testing. Once a test is written, we have excellent continuous integration systems like Jenkins or Cruise Control to validate the tests against any changes made on the code.
However, the CI system is only as effective as the test. If the test does not cover all use cases, then there is no point in running the tests in CI.
The article speaks solely from the viewpoint of a C programmer working on a Linux PC or similar systems. All large scale, usable C code is written in the form of blocks of code enclosed in functions. A block can be a set of simple execution statements or a logical branch. The ideal test code should logically call all the functions and execute all statements in the blocks. The percentage of lines of actual code that gets executed when a test code runs is called the coverage of the test code.
More the number of lines of code that is tested, less is the probability to get a last minute surprise bug. Static code coverage analysis is done by analyzing the test code and the actual code to primarily estimate the function call coverage.
Static code coverage analysis is much faster and simpler since the generated object file need not be executed. This is particularly handy in the case of small scale embedded systems. Dynamic code coverage analysis is much more elaborate and requires the test code to be executed on the target. It also requires the object file to be generated with special compilation options.
However, it gives much more detailed analysis of how effective the test is. For obvious reasons, it is not practical to manually compute the coverage of a test code. Thus we have some tools that can compute the coverage of our test code for us. As per Wikipedia, Gcov is a source code coverage analysis and statement-by-statement profiling tool.
Gcov generates exact counts of the number of times each statement in a program is executed and annotates source code to add instrumentation.
When using gcov, you must first compile your program with —coverage GCC option. This tells the compiler to generate additional information needed by gcov basically a flow graph of the program and also includes additional code in the object files for generating the extra profiling information needed by gcov.
These additional files. Once the object file is generated, execute it once to get the profile file. To illustrate the usage of gcov, we will consider a very minimal library lib. Now, execute the test code object file. This will generate the following files. Now we have all the inputs required for gcov to generate the coverage report. To generate the coverage report, run the following command. Coverage summary will be displayed as below when gcov finishes execution:.
Detailed coverage report will be available in the lib. Each block is marked by a line with the same line number as the last line of the block and the number of branch and calls in the block. Each function is preceded with a line showing the number of times the function is called, number of times it returns and the percentage of function blocks that were executed.
Each line of executable code contains the number of times the line was executed and the actual source code line number. Any line that was not executed will have in place of the execution count. The Linux testing project LTP has come up with a tool called lcov that provides a more user-friendly graphical visualization of the gcov output. It generates html files and integrates well with web based CI systems.
To make lcov generate html reports for you, give the following commands once the gcno and gcda files are generated. As we have seen so far, running the program to be profiled will cause profile output to be generated. This places a restriction that the target system should have the same directory structure.
The program will try to create the needed directory structure, if it is not already present. As per the gnu GCOV documentation, redirection can be done with the help of two execution environment variables.
Thanks for this blog on code coverage. It worked perfectly on Linux. Are you talking about an embedded system with or without a filesystem? If a standard filesystem is present, the above method should work.
If it is a system without a FS, you will have to make corresponding provisions to store the gcda files. My question is related to GCOV I know that in order to get coverage details we need to add -fprofile-arcs and -ftest-coverage flags.
In my case project contain a lot of files actually project was not created by myself , i know how to make that project by using make command.
Myquestion is: How I can add the above flags while making, in such a way that after execution of the projects i need to get coverage of the execution in project source files. There is a unix stackexchange entry for a similar problem. I am not actively working on this. This is a good post, concise and to the point. You are commenting using your WordPress. You are commenting using your Google account.
You are commenting using your Twitter account. You are commenting using your Facebook account. Notify me of new comments via email. Notify me of new posts via email. Create a free website or blog at WordPress. The article speaks solely from the viewpoint of a C programmer working on a Linux PC or similar systems What is coverage? There are two types of coverage analysis that is possible.
This will generate the following files lib. Like this: Like Loading Matiz said. Kiran said. Shakukaa said. Anurag Shamra said. Sean M. Serg said. Masako Heileman said. Leave a Reply Cancel reply Enter your comment here Fill in your details below or click an icon to log in:.
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Analyzing Code Coverage with gcov
Gcov is a source code coverage analysis and statement-by-statement profiling tool. Gcov generates exact counts of the number of times each statement in a program is executed and annotates source code to add instrumentation. The gcov utility gives information on how often a program executes segments of code. The gcov utility does not produce any time-based data and works only on code compiled with the GCC suite. The manual claims it is not compatible with any other profiling or test coverage mechanism,  but it works with llvm -generated files, too. The options -fprofile-arcs -ftest-coverage should be used to compile the program for coverage analysis first option to record branch statistics and second to save line execution count ; -fprofile-arcs should also be used to link the program. It takes source files as command-line arguments and produces an annotated source listing.
gcov - Unix, Linux Command
Before releasing any amount of code, developers usually test their work to tune performance and prove that the software works as intended. But often, validation is quite difficult, even if the application is simple. If the test suite is thorough, all of the features of the code can be exercised and be shown to work. But how thorough is thorough? In theory, a completely thorough test suite would test all circumstances, validate all of the results, and exercise every single line of code, demonstrating that no code is "dead. Validating results can be done in any number of ways since output is typically tangible in one form or another, but how do you make sure that all of your code was executed? As mentioned above, it's ideal to find dead code and get rid of it.
What is coverage?
Gcovr provides a utility for managing the use of the GNU gcov utility and generating summarized code coverage results. This command is inspired by the Python coverage. The gcovr command can produce different kinds of coverage reports:. Thus, gcovr can be viewed as a command-line alternative to the lcov utility, which runs gcov and generates an HTML-formatted report.